Deep learning-based near-field effect correction method for Controlled Source Electromagnetic Method and application.

ABSTRACT: Addressing the impact of near-field effects in the Controlled Source Electromagnetic Method(CSEM) has long been a focal point in the realm of geophysical exploration. Therefore, we propose a deep learning-based near-field correction method for controlled-source electromagnetic methods. Initially, diverse datasets for a layered geologic model are generated through forward simulation. Building upon the characteristics of near-field effects, a deep learning network utilizing LSTM-CNN is meticulously constructed. Multiple experiments are executed to scrutinize the network's effectiveness in mitigating near-field effects and its resilience against noise. Following this, the proposed method is applied to actual CSEM data to validate its applicability in practice. The method is subsequently teste

SUBMITTER: Luo W 

PROVIDER: S-EPMC11554044 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature